





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case								=							logging.get_logger(__name__)

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[int]      ,				snake_case__	:str=False  )   ->    List[Any]:
  _lowercase						    =			[]
  for i in range(config.num_hidden_layers  ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")  )
    rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")  )
    rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")  )
    rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")  )
    rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")  )
    rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")  )
    rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")  )
    rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")  )
    rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")  )
    rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")  )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'vit.embeddings.cls_token'),
          ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'vit.embeddings.position_embeddings'),
      ]  )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
        ]  )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    _lowercase						    =			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'  ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]  )

  return rename_keys

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]=False  )   ->    Union[str, Any]:
  for i in range(config.num_hidden_layers  ):
    if base_model:
      _lowercase						    =			''
    else:
      _lowercase						    =			'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _lowercase						    =			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""  )
    _lowercase						    =			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""  )
    # next, add query, keys and values (in that order) to the state dict
    _lowercase						    =			in_proj_weight[
        : config.hidden_size, :
    ]
    _lowercase						    =			in_proj_bias[: config.hidden_size]
    _lowercase						    =			in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _lowercase						    =			in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _lowercase						    =			in_proj_weight[
        -config.hidden_size :, :
    ]
    _lowercase						    =			in_proj_bias[-config.hidden_size :]

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, Any]  )   ->    Tuple:
  _lowercase						    =			['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(snake_case__      ,				snake_case__  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:Tuple      ,				snake_case__	:Tuple  )   ->    Optional[Any]:
  _lowercase						    =			dct.pop(snake_case__  )
  _lowercase						    =			val

def 				SCREAMING_SNAKE_CASE__							(	)   ->    List[str]:
  _lowercase						    =			'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase						    =			Image.open(requests.get(snake_case__      ,				stream=snake_case__  ).raw  )
  return im

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:Dict      ,				snake_case__	:List[str]=True  )   ->    str:
  _lowercase						    =			ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    _lowercase						    =			8
  # set labels if required
  if not base_model:
    _lowercase						    =			1000
    _lowercase						    =			'huggingface/label-files'
    _lowercase						    =			'imagenet-1k-id2label.json'
    _lowercase						    =			json.load(open(hf_hub_download(snake_case__      ,				snake_case__      ,				repo_type='dataset'  )      ,				'r'  )  )
    _lowercase						    =			{int(snake_case__  ): v for k, v in idalabel.items()}
    _lowercase						    =			idalabel
    _lowercase						    =			{v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    _lowercase						    =			384
    _lowercase						    =			1536
    _lowercase						    =			12
    _lowercase						    =			6

  # load original model from torch hub
  _lowercase						    =			torch.hub.load('facebookresearch/dino:main'      ,				snake_case__  )
  original_model.eval()

  # load state_dict of original model, remove and rename some keys
  _lowercase						    =			original_model.state_dict()
  if base_model:
    remove_classification_head_(snake_case__  )
  _lowercase						    =			create_rename_keys(snake_case__      ,				base_model=snake_case__  )
  for src, dest in rename_keys:
    rename_key(snake_case__      ,				snake_case__      ,				snake_case__  )
  read_in_q_k_v(snake_case__      ,				snake_case__      ,				snake_case__  )

  # load HuggingFace model
  if base_model:
    _lowercase						    =			ViTModel(snake_case__      ,				add_pooling_layer=snake_case__  ).eval()
  else:
    _lowercase						    =			ViTForImageClassification(snake_case__  ).eval()
  model.load_state_dict(snake_case__  )

  # Check outputs on an image, prepared by ViTImageProcessor
  _lowercase						    =			ViTImageProcessor()
  _lowercase						    =			image_processor(images=prepare_img()      ,				return_tensors='pt'  )
  _lowercase						    =			encoding['pixel_values']
  _lowercase						    =			model(snake_case__  )

  if base_model:
    _lowercase						    =			original_model(snake_case__  )
    assert torch.allclose(snake_case__      ,				outputs.last_hidden_state[:, 0, :]      ,				atol=1E-1  )
  else:
    _lowercase						    =			original_model(snake_case__  )
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(snake_case__      ,				outputs.logits      ,				atol=1E-3  )

  Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
  model.save_pretrained(snake_case__  )
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
  image_processor.save_pretrained(snake_case__  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""dino_vitb16""",
          type=str,
          help="""Name of the model trained with DINO you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--base_model""",
          action="""store_true""",
          help="""Whether to only convert the base model (no projection head weights).""",
      )

      parser.set_defaults(base_model=True)
      snake_case								=							parser.parse_args()
      convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





'''simple docstring'''


import math


def     SCREAMING_SNAKE_CASE							(   a_						:				float					,       a_						:				float      ):

  if initial_intensity < 0:
    raise ValueError('The value of intensity cannot be negative'      )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 360:
    raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'      )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(a_      )      ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



